Quantification of protein isoforms in mesenchymal stem cells by reductive dimethylation of lysines in intact proteins
Bibliographic record
Abstract
Mass spectrometry (MS)-based quantification of highly homologous proteins in complex samples has proven difficult due to subtle sequence variations and the wide dynamic range of protein isoforms present. Herein, we report the use of reductive dimethylation on intact proteins to quantitatively compare protein isoform expression in the nucleus and cytoplasm of mesenchymal stem cells (MSC) and normal stroma. By coupling fixed-charge MS/MS scanning, high-resolution UPLC FT-MS data-dependent acquisition and MASCOT-based data mining, hydrogen/deuterium-labeled dimethyl-lysine peptides were simultaneously captured allowing the accurate comparison of 123 protein isoforms in parallel LC MS/MS runs. Thirty-four isoforms were identified that had expression levels specific to MSC. Where possible, proteomic analyses were verified by Western blotting and were demonstrated to be divergent from the level of gene transcription detected for certain proteins. Our analysis provides a protein isoform signature specific to MSC and demonstrates the suitability of dimethyl-lysine labeling on intact proteins for quantifying highly homologous proteins on a proteome-wide scale.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".